Highlights

Zoom Link: https://hku.zoom.us/j/3837289217?omn=95617077246 Abstract Analog computing-in-memory accelerators promise ultra-low-power, on-device AI by reducing data transfer and energy usage. Yet inherent device variations and high energy consumption for analog-digital conversion continue to hinder their wide-scale adoption in mainstream systems. To address these issues, this presentation will introduce WireLightning, a novel capacitive-computing accelerator featuring a mixed-signal architecture […]

Zoom Link : https://hku.zoom.us/j/91765972342?pwd=CHXoKEknnfPc6zbhHCADi7A1abVUyI.1 Abstract As deep learning is increasingly applied in medical image analysis, developing efficient and accurate models has become crucial. However, traditional deep learning methods usually require large amounts of annotated data, posing a significant challenge in medical imaging due to complex data collection and high annotation costs. Furthermore, privacy and security […]

Zoom Link: https://hku.zoom.us/j/95300634244 Abstract In the realm of activity detection for massive machine-type communications, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However, traditional activity detection methods are typically designed for a single type of channel model, which does not reflect the […]

Zoom Link: https://hku.zoom.us/j/91757354553?pwd=tHpInMTglaIVMJLek0ydP0vddHihh8.1  Meeting ID: 917 5735 4553 Passcode: 587193 Abstract Existing Video-to-Audio (V2A) models typically generate sound based solely on visual input, offering limited user control. To address this limitation, we propose a multimodal controllable V2A system that conditions audio generation on a variety of user inputs– such as text, images, or audio– in […]

Zoom Link: https://hku.zoom.us/j/98147018160?pwd=KEuU1XQtISq3HQpWJyF6itZMJ1hYY5.1 Meeting ID: 981 4701 8160 Password: 294231 Abstract Accurately estimating morphological changes of the Posterior Eyeball Shape (PES) is a critical task in ophthalmology, since the PES is a crucial factor in many clinical applications, such as myopia prevention, surgical planning, and disease screening. However, existing imaging devices are constrained by limited field-of-view (FOV) […]

Zoom Link: https://hku.zoom.us/j/93486553339 Abstract The emergence of distributed Mixture-of-Experts (DMoE) systems, which deploy expert models at edge nodes, offers a pathway to achieving connected intelligence in sixth-generation (6G) mobile networks and edge artificial intelligence (AI). However, current DMoE systems lack an effective expert selection algorithm to address the simultaneous task-expert relevance and channel diversity inherent […]

Zoom Link: https://hku.zoom.us/j/92719085356?pwd=09aQ3vjvg9bhXcObBYxjNtj4UVx5V4.1 Abstract Mixed-chloride/bromide perovskite nanocrystals (PeNCs) are known for their advantages in pure blue emission, but often suffer from halogen segregation. This study investigates the ligand exchange process with different ion pair combinations to improve stability. Surprisingly, altering the ligand ion combinations leads to a deviation from pure blue emission in CsPbBrxCl3-x nanocrystals […]

Abstract 3D Dynamic Meshes can deliver engaging experiences in various applications, but the storage and transmission demands associated with these data structures can be prohibitive. We address this challenge with an efficient compression technique leveraging embedded key nodes. The temporal motion of each vertex is formulated as a distance-weighted combination of transformations from neighboring key […]

Zoom Link: https://hku.zoom.us/j/95380440070 Abstract Complex-valued convolution neural networks (CVCNNs) have been recently applied for modulation recognition (MR), due to its ability to capture the relationship between the real and imaginary parts of the received signal. On the other hand, the transformer model has been shown to be distinguished in MR by its superior capability to […]

Abstract Recent advances in artificial intelligence (AI), especially in large language models (LLMs), have dramatically increased model sizes and computational demands, significantly straining computing system capabilities. This issue is particularly acute in resource-constrained edge AI scenarios, where efficient hardware acceleration of compute-intensive tasks and optimization of data reuse to minimize costly data transfers are essential. […]

Zoom Link: https://hku.zoom.us/j/97430126742?pwd=ou6CUPNMjhlrmRbwUKRa8aTHi6PjYX.1 Meeting ID: 974 3012 6742 Password: 967270 Abstract The human brain operates as a sophisticated spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by leveraging prior knowledge. Impressively, it accomplishes this with minimal energy consumption, relying on event-driven signals that travel through its intricate structure. However, replicating […]

Zoom Link: https://hku.zoom.us/j/99273671426?pwd=bm3VeyFWXnLAlUIBBXJDGAmMfzoKJ5.1 Abstract 6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computation (AirComp), exploits channels’ waveform […]

Abstract As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent health monitoring and intervention pipeline seems to be within reach. How do we get there? In this talk, I will introduce a comprehensive pipeline that […]

Abstract Terahertz (THz) imaging technology is growing rapidly due to its potential applications in material exploration, non-destructive evaluation, industrial inspection, and bioinformatics. However, the practical feasibility of THz imaging systems is significantly constrained by the low efficiency of active THz devices, long imaging acquisition time, insufficient use of THz signal datasets, and their bulky nature. […]

Seminar (1): Navigating Excellence in Transnational Engineering Education: The QMUL-BUPT Joint Programme Experience Speaker: Prof. Michael CHAI, Professor of Internet of Things, Queen Mary University of London Abstract: As global demand grows for engineering graduates equipped with both academic excellence and international perspectives, transnational education (TNE) plays a vital role in redefining the boundaries of global […]

Abstract Intelligent reflecting surface (IRS) is envisioned to be a promising 6G technology which changes wireless communications from “adapting to wireless channels” to “changing wireless channels”. However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming, are model-based designs and are difficult to be realized in practical and complex radio environment. To create […]

Abstract The forthcoming sixth generation (6G) wireless networks will be one of the galvanizing technologies for future advanced and autonomous transportation systems. 6G wireless will provide communication services with stringent requirements which are necessary for autonomous driving and on-flight Internet connectivity. Introduction of AI-empowered 6G would guarantee a more intelligent, efficient and secure transportation system. […]

🚀 Join the Excitement of Robotics! Competition Details Date: 19 July 2025 (SAT) Time: Check-in begins at 9:00 AM Venue: Innovation Wing One, G/F, Hui Oi Chow Science Building, The University of Hong Kong (HKU) (directions to the venue) Competition Description Engage your autonomous robots in thrilling challenges: - Maze Navigation 迷宮探索 ​ - Rescue […]

Abstract Finite symmetric groups Sn are essential in fields such as combinatorics, physics, and chemistry. However, learning a probability distribution over Sn poses significant challenges due to its intractable size and discrete nature. We introduce SymmetricDiffusers, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over Sn by decomposing it […]